For human detection, we opted in this work for the recent Scaled YOLOv4 model [1]}. Authors of [1]} proposed initially the YOLOv4 model, which inherits the fully convolutional architecture of its ancestors. Although this object detection model achieved state-of-the-art results, Bochkovskiy et al. further enhanced their architecture by readjusting the backbone, adding more Cross Stage Partial (CSP) connections and scaling up the model to get the best speed/accuracy trade-off. This novel architecture, called Scaled YOLOv4, has been proven to achieve high detection results. Indeed, as shown in [1]}, ground-breaking results of 55.4% in average precision (AP) on the Microsoft Common Objects in Context (MS COCO) dataset with a relatively high inference speed is realized.
